100% screening economic order quantity model under shortage and delay in payment

It is for a long time that the Economic Order Quantity(EOQ) model has been successfully applied to inventory management. This paper studies a multiproduct EOQ problem in which the defective items will be screened out by 0 screening process and will be sold after the screening period. Delay in payment is permissible though payment should be made during the grace period and the warehouse capacity is limited. Otherwise, there will be an additional penalty cost for late payment so the retailer would not be able tobuy products at discount prices.All-units and incremental discounts are considered for the products which dependon the order’s quantity just like the permissible delay in payment. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to solve the proposed model and numerical examples are provided for better illustrations

Closed-form equations for optimal lot sizing in deterministic EOQ models with exchangeable imperfect ACquality items

In this paper, the optimal lot size for batches with exchangeable imperfect items is derived where the delay time for the exchange process depends on the quantity of imperfect items. This delay in exchange may or may not lead into shortage. The initial received lot is 100% screened. After the screening process, an order to exchange defective products takes place. The imperfect items are held in buyer's warehouse until the arrival of the exchange lot from the supplier for which, after another 100% screening process, imperfect items are sold at a lower price in a single batch. Two possible situations in which 1) there will not be any shortage, and 2) there will be a shortage that is fulfilled before the end of the replenishment cycle, are investigated. Proper mathematical models are developed and closed-form formulae are derived. Numerical examples are provided not only to demonstrate application of the proposed model, but also to analyze and compare the results obtained employing the proposed model and the ones gained using the classical economic order quantity model

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Dietary amino acid patterns and cardiometabolic risk factors among subjects with obesity; a cross-sectional study

Abstract Background The prevalence of obesity is a growing global public health concern. Certain dietary amino acids have been shown to have a potential therapeutic role in improving metabolic syndrome parameters and body composition in individuals with obesity. However, some amino acids have been linked to an increased risk of cardiometabolic disorders. This cross-sectional study aims to investigate the association between dietary amino acid patterns and cardiometabolic risk factors in individuals with obesity. Methods This cross-sectional study included 335 participants with obesity (57.9% males and 41.5% females) from Tabriz and Tehran, Iran. The participants were between the ages of 20–50, with a body mass index (BMI) of 30 kg/m2 or higher, and free from certain medical conditions. The study examined participants’ general characteristics, conducted anthropometric assessments, dietary assessments, and biochemical assessments. The study also used principal component analysis to identify amino acid intake patterns and determined the association between these patterns and cardiometabolic risk factors in individuals with obesity. Results Upon adjusting for potential confounders, the study found that individuals in the third tertiles of pattern 1 and 2 were more likely to have lower LDL levels (OR = 0.99 and 95% CI (0.98–0.99)) for both. Additionally, a significant decrease in total cholesterol was observed in the third tertiles of pattern 2 in model II (OR = 0.99, 95% CI (0.98–0.99)). These findings suggest a potential cardioprotective effect of these amino acid patterns in managing cardiometabolic risk factors in individuals with obesity. Conclusions This study found that two identified amino acid patterns were associated with lower serum LDL and total cholesterol levels, while a third pattern was associated with higher serum triglycerides. The specific amino acids contributing to these patterns highlight the importance of targeted dietary interventions in managing cardiometabolic risk factors in individuals with obesity

A bi-objective inventory model to minimize cost and stock out time under backorder shortages and screening

Although minimizing costs is the main objective in inventory models, meeting customer's needs is another basic goal of many companies. Especially in the case shortages are allowed, the waiting time a customer spends to receive his/her backlogged shortage is an important factor that affects maintaining with the company. As defining an accurate shortage cost which includes cost of losing customers and cost of damaging brand image is not possible for many companies, the above two objectives are considered in this paper to develop a bi-objective inventory model, in which the total cost of a retailer and his customers\u2019 stock out times are minimized. In this model, the order size, the maximum backordering quantity, and the number of inspectors are defined as the decision variables, where a 100% screening process with the rate higher than the demand rate is used to screen out the items. After screening, the non-conforming items are stored in the warehouse, where they will be exchanged with new items when a new order is arrived at the end of the cycle. The bi-objective optimization problem is solved using the non-dominated sorting genetic algorithm (NSGA-II). As there is no benchmark available in the literature, another multi-objective optimization algorithm called the multi-objective particle swarm optimization (MOPSO) is employed to validate the result and to evaluate the performance of NSGA-II. Computational results of solving some randomly generated numerical examples are in favor of MOPSO

A bi-objective inventory model to minimize cost and stock out time under backorder shortages and screening

Although minimizing costs is the main objective in inventory models, meeting customer's needs is another basic goal of many companies. Especially in the case shortages are allowed, the waiting time a customer spends to receive his/her backlogged shortage is an important factor that affects maintaining with the company. As defining an accurate shortage cost which includes cost of losing customers and cost of damaging brand image is not possible for many companies, the above two objectives are considered in this paper to develop a bi-objective inventory model, in which the total cost of a retailer and his customers’ stock out times are minimized. In this model, the order size, the maximum backordering quantity, and the number of inspectors are defined as the decision variables, where a 100% screening process with the rate higher than the demand rate is used to screen out the items. After screening, the non-conforming items are stored in the warehouse, where they will be exchanged with new items when a new order is arrived at the end of the cycle. The bi-objective optimization problem is solved using the non-dominated sorting genetic algorithm (NSGA-II). As there is no benchmark available in the literature, another multi-objective optimization algorithm called the multi-objective particle swarm optimization (MOPSO) is employed to validate the result and to evaluate the performance of NSGA-II. Computational results of solving some randomly generated numerical examples are in favor of MOPSO

A single-retailer multi-supplier multi-product inventory model with destructive testing acceptance sampling and inflation

4sìreservedIn this paper, a multi-product inventory problem is investigated in which a retailer buys items from different suppliers based on their purchasing costs and defective rates. Due to the warehouse and staff constraints involved, the inventory cycle consists of two parts. The first part corresponds to a screening period in which a destructive testing acceptance-sampling plan is used to accept or reject a lot. The other part is for selling the items. In the screening period, a lot that is rejected is returned to the suppliers where another lot is claimed for substitution at no cost. Shortage occurs during the screening period and the defective items are sold at a lower price at the end of the second part of the cycle. As we show that the problem belongs to the class of NP-hard problems, a particle swarm optimization (PSO) and a genetic algorithm (GA) is used to solve it.mixedBehzad Maleki Vishkaei, Seyed Taghi Akhavan Niaki, Milad Farhangi, Iraj MahdaviMaleki Vishkaei, Behzad; Taghi Akhavan Niaki, Seyed; Farhangi, Milad; Mahdavi, Ira

Optimal lot sizing in screening processes with returnable defective items

This paper is an extension of Hsu and Hsu (Int J Ind Eng Comput 3(5):939\u2013948, 2012) aiming to determine the optimal order quantity of product batches that contain defective items with percentage nonconforming following a known probability density function. The orders are subject to 100 % screening process at a rate higher than the demand rate. Shortage is backordered, and defective items in each ordering cycle are stored in a warehouse to be returned to the supplier when a new order is received. Although the retailer does not sell defective items at a lower price and only trades perfect items (to avoid loss), a higher holding cost incurs to store defective items. Using the renewal-reward theorem, the optimal order and shortage quantities are determined. Some numerical examples are solved at the end to clarify the applicability of the proposed model and to compare the new policy to an existing one. The results show that the new policy provides better expected profit per time